Single-sequence and profile-based prediction of RNA solvent accessibility using dilated convolutional neural network | Zendy

Anil Kumar Hanumanthappa | Zendy; Jaswinder Singh | Zendy; Kuldip K. Paliwal | Zendy; Jaspreet Singh | Zendy; Yaoqi Zhou | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Single-sequence and profile-based prediction of RNA solvent accessibility using dilated convolutional neural network

Author(s) -

Anil Kumar Hanumanthappa,

Jaswinder Singh,

Kuldip K. Paliwal,

Jaspreet Singh,

Yaoqi Zhou

Publication year - 2020

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btaa652

Subject(s) - rna , computer science , convolutional neural network , sequence (biology) , set (abstract data type) , test set , pattern recognition (psychology) , computational biology , coding (social sciences) , structural motif , algorithm , artificial intelligence , data mining , mathematics , chemistry , genetics , biology , biochemistry , statistics , gene , programming language

RNA solvent accessibility, similar to protein solvent accessibility, reflects the structural regions that are accessible to solvents or other functional biomolecules, and plays an important role for structural and functional characterization. Unlike protein solvent accessibility, only a few tools are available for predicting RNA solvent accessibility despite the fact that millions of RNA transcripts have unknown structures and functions. Also, these tools have limited accuracy. Here, we have developed RNAsnap2 that uses a dilated convolutional neural network with a new feature, based on predicted base-pairing probabilities from LinearPartition.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research